L. Vanessa Smith
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199670086
- eISBN:
- 9780191749469
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199670086.003.0008
- Subject:
- Economics and Finance, Macro- and Monetary Economics
This chapter considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end the global vector autoregressive (GVAR) model ...
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This chapter considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end the global vector autoregressive (GVAR) model introduced in Chapter 2, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1-2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. The effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, the heterogeneity of the economies considered, as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output and inflation.Less
This chapter considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end the global vector autoregressive (GVAR) model introduced in Chapter 2, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1-2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. The effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, the heterogeneity of the economies considered, as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output and inflation.
Lorenzo A. Preve and Virginia Sarria-Allende
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780199737413
- eISBN:
- 9780199775637
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199737413.003.0004
- Subject:
- Economics and Finance, Financial Economics
In this chapter, we introduce the most commonly used ratios, classified according to four broad categories that are fairly standardized; in particular, we can organize a firm's financial statement ...
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In this chapter, we introduce the most commonly used ratios, classified according to four broad categories that are fairly standardized; in particular, we can organize a firm's financial statement information into ratios to examine the firm's profitability, liquidity, operating efficiency, and financial leverage. We start by analyzing the way these ratios are built and how they should be interpreted. Next, we consider some other ratios, based on market data. Finally, we discuss how to realize a comprehensive financial analysis of a firm using these ratios.Less
In this chapter, we introduce the most commonly used ratios, classified according to four broad categories that are fairly standardized; in particular, we can organize a firm's financial statement information into ratios to examine the firm's profitability, liquidity, operating efficiency, and financial leverage. We start by analyzing the way these ratios are built and how they should be interpreted. Next, we consider some other ratios, based on market data. Finally, we discuss how to realize a comprehensive financial analysis of a firm using these ratios.
Niels Haldrup, Mika Meitz, and Pentti Saikkonen (eds)
- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199679959
- eISBN:
- 9780191760136
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199679959.001.0001
- Subject:
- Economics and Finance, Econometrics
This book is a collection of 14 original research articles presented at the conference Nonlinear Time Series Econometrics that was held in Ebeltoft, Denmark, in June 2012. The conference gathered ...
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This book is a collection of 14 original research articles presented at the conference Nonlinear Time Series Econometrics that was held in Ebeltoft, Denmark, in June 2012. The conference gathered several eminent time series econometricians to celebrate the work and outstanding career of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The book is divided into four broad themes that all reflect Timo Teräsvirta’s work and methodology: testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent the state of the art in econometrics, such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had, and will continue to have, on the profession.Less
This book is a collection of 14 original research articles presented at the conference Nonlinear Time Series Econometrics that was held in Ebeltoft, Denmark, in June 2012. The conference gathered several eminent time series econometricians to celebrate the work and outstanding career of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The book is divided into four broad themes that all reflect Timo Teräsvirta’s work and methodology: testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent the state of the art in econometrics, such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had, and will continue to have, on the profession.
